An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Following publication, it was noticed that the horizontal brackets labelling the two groups of precisions present in Equation 7 are incorrectly rendered in the PDF version of this Data Descriptor. The correct Equation 7 is as follows: (Formula presented.) (Formula presented.) In addition, in the Biomass subsection of the Methods section in both the HTML and PDF versions, the term “ESUs” is incorrectly rendered as “ESU’s” and the term ESUBs is incorrectly rendered as “ESUB’s” Finally, throughout the manuscript, references to sections and subsections include the prefixes “sec:” and “subsec:”, respectively. These prefixes and any hyphen between the reference words that follow the prefixes can be ignored.

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Space-based remote sensing imagery can provide a valuable and cost-effective set of observations for mapping crop-productivity differences. The effectiveness of such signals is dependent on several conditions that are related to crop and sensor characteristics. In this paper, we present the dynamic behavior of signals from five Synthetic Aperture Radar (SAR) sensors and optical sensors with growing sugarcane, focusing on saturation effects and the influence of precipitation events. In addition, we analyzed the level of agreement within and between these spaceborne datasets over space and time. As a result, we produced a list of conditions during which the acquisition of satellite imagery is most effective for sugarcane productivity monitoring. For this, we analyzed remote sensing data from two C-band SAR (Sentinel-1 and Radarsat-2), one L-band SAR (ALOS-2), and two optical sensors (Landsat-8 and WorldView-2), in conjunction with detailed ground-reference data acquired over several sugarcane fields in the state of São Paulo, Brazil. We conclude that satellite imagery from L-band SAR and optical sensors is preferred for monitoring sugarcane biomass growth in time and space. Additionally, C-band SAR imagery offers the potential for mapping spatial variations during specific time windows and may be further exploited for its precipitation sensitivity.@en

In order to make effective decisions on sustainable development, it is essential for sugarcane-producing countries to take into account sugarcane acreage and sugarcane production dynamics. The availability of sugarcane biophysical data along the growth season is key to an effective mapping of such dynamics, especially to tune agronomic models and to cross-validate indirect satellite measurements. Here, we introduce a dataset comprising 3,500 sugarcane observations collected from October 2014 until October 2015 at four fields in the São Paulo state (Brazil). The campaign included both non-destructive measurements of plant biometrics and destructive biomass weighing procedures. The acquisition plan was designed to maximize cost-effectiveness and minimize field-invasiveness, hence the non-destructive measurements outnumber the destructive ones. To compensate for such imbalance, a method to convert the measured biometrics into biomass estimates, based on the empirical adjustment of allometric models, is proposed. In addition, the paper addresses the precisions associated to the ground measurements and derived metrics. The presented growth dynamics and associated precisions can be adopted when designing new sugarcane measurement campaigns.@en

SAR as an active remote sensing technique is capable of providing insights into the physical features of agricultural vegetation. However, the noisy nature of SAR signals makes the direct conversion to effective productivity metrics challenging. This study sheds light on the effect of gaps present in a sugarcane field on L-band and C-band SAR signals and demonstrates the variability of this effect with changing spatial averaging windows, changing precipitation conditions and changing vegetation height.@en